Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations649
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory728.9 KiB
Average record size in memory1.1 KiB

Variable types

Categorical20
Numeric5
Boolean8

Alerts

G1 is highly overall correlated with G2 and 1 other fieldsHigh correlation
G2 is highly overall correlated with G1 and 1 other fieldsHigh correlation
G3 is highly overall correlated with G1 and 1 other fieldsHigh correlation
failures is highly imbalanced (59.9%) Imbalance
schoolsup is highly imbalanced (51.6%) Imbalance
paid is highly imbalanced (67.2%) Imbalance
higher is highly imbalanced (51.1%) Imbalance
absences has 244 (37.6%) zeros Zeros
G2 has 7 (1.1%) zeros Zeros
G3 has 15 (2.3%) zeros Zeros

Reproduction

Analysis started2025-03-19 14:21:12.502767
Analysis finished2025-03-19 14:21:17.956083
Duration5.45 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

school
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size37.5 KiB
GP
423 
MS
226 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1298
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGP
2nd rowGP
3rd rowGP
4th rowGP
5th rowGP

Common Values

ValueCountFrequency (%)
GP 423
65.2%
MS 226
34.8%

Length

2025-03-19T15:21:17.993208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.030371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gp 423
65.2%
ms 226
34.8%

Most occurring characters

ValueCountFrequency (%)
G 423
32.6%
P 423
32.6%
M 226
17.4%
S 226
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1298
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 423
32.6%
P 423
32.6%
M 226
17.4%
S 226
17.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1298
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 423
32.6%
P 423
32.6%
M 226
17.4%
S 226
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 423
32.6%
P 423
32.6%
M 226
17.4%
S 226
17.4%

sex
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
F
383 
M
266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 383
59.0%
M 266
41.0%

Length

2025-03-19T15:21:18.070331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.106104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f 383
59.0%
m 266
41.0%

Most occurring characters

ValueCountFrequency (%)
F 383
59.0%
M 266
41.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 649
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 383
59.0%
M 266
41.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 649
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 383
59.0%
M 266
41.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 383
59.0%
M 266
41.0%

age
Real number (ℝ)

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.744222
Minimum15
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-03-19T15:21:18.139313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q116
median17
Q318
95-th percentile19
Maximum22
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2181376
Coefficient of variation (CV)0.072749731
Kurtosis0.071508585
Mean16.744222
Median Absolute Deviation (MAD)1
Skewness0.41679538
Sum10867
Variance1.4838593
MonotonicityNot monotonic
2025-03-19T15:21:18.183315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
17 179
27.6%
16 177
27.3%
18 140
21.6%
15 112
17.3%
19 32
 
4.9%
20 6
 
0.9%
21 2
 
0.3%
22 1
 
0.2%
ValueCountFrequency (%)
15 112
17.3%
16 177
27.3%
17 179
27.6%
18 140
21.6%
19 32
 
4.9%
20 6
 
0.9%
21 2
 
0.3%
22 1
 
0.2%
ValueCountFrequency (%)
22 1
 
0.2%
21 2
 
0.3%
20 6
 
0.9%
19 32
 
4.9%
18 140
21.6%
17 179
27.6%
16 177
27.3%
15 112
17.3%

address
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
U
452 
R
197 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 452
69.6%
R 197
30.4%

Length

2025-03-19T15:21:18.234046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.277963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
u 452
69.6%
r 197
30.4%

Most occurring characters

ValueCountFrequency (%)
U 452
69.6%
R 197
30.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 649
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 452
69.6%
R 197
30.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 649
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 452
69.6%
R 197
30.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 452
69.6%
R 197
30.4%

famsize
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.2 KiB
GT3
457 
LE3
192 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1947
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGT3
2nd rowGT3
3rd rowLE3
4th rowGT3
5th rowGT3

Common Values

ValueCountFrequency (%)
GT3 457
70.4%
LE3 192
29.6%

Length

2025-03-19T15:21:18.324276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.368029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gt3 457
70.4%
le3 192
29.6%

Most occurring characters

ValueCountFrequency (%)
3 649
33.3%
G 457
23.5%
T 457
23.5%
L 192
 
9.9%
E 192
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1298
66.7%
Decimal Number 649
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 457
35.2%
T 457
35.2%
L 192
14.8%
E 192
14.8%
Decimal Number
ValueCountFrequency (%)
3 649
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1298
66.7%
Common 649
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 457
35.2%
T 457
35.2%
L 192
14.8%
E 192
14.8%
Common
ValueCountFrequency (%)
3 649
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 649
33.3%
G 457
23.5%
T 457
23.5%
L 192
 
9.9%
E 192
 
9.9%

Pstatus
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
T
569 
A
80 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowT
3rd rowT
4th rowT
5th rowT

Common Values

ValueCountFrequency (%)
T 569
87.7%
A 80
 
12.3%

Length

2025-03-19T15:21:18.413656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.459670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
t 569
87.7%
a 80
 
12.3%

Most occurring characters

ValueCountFrequency (%)
T 569
87.7%
A 80
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 649
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 569
87.7%
A 80
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 649
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 569
87.7%
A 80
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 569
87.7%
A 80
 
12.3%

Medu
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
2
186 
4
175 
1
143 
3
139 
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row4
5th row3

Common Values

ValueCountFrequency (%)
2 186
28.7%
4 175
27.0%
1 143
22.0%
3 139
21.4%
0 6
 
0.9%

Length

2025-03-19T15:21:18.534660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.576698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 186
28.7%
4 175
27.0%
1 143
22.0%
3 139
21.4%
0 6
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 186
28.7%
4 175
27.0%
1 143
22.0%
3 139
21.4%
0 6
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 186
28.7%
4 175
27.0%
1 143
22.0%
3 139
21.4%
0 6
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 186
28.7%
4 175
27.0%
1 143
22.0%
3 139
21.4%
0 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 186
28.7%
4 175
27.0%
1 143
22.0%
3 139
21.4%
0 6
 
0.9%

Fedu
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
2
209 
1
174 
3
131 
4
128 
0
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 209
32.2%
1 174
26.8%
3 131
20.2%
4 128
19.7%
0 7
 
1.1%

Length

2025-03-19T15:21:18.623821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.666662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 209
32.2%
1 174
26.8%
3 131
20.2%
4 128
19.7%
0 7
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2 209
32.2%
1 174
26.8%
3 131
20.2%
4 128
19.7%
0 7
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 209
32.2%
1 174
26.8%
3 131
20.2%
4 128
19.7%
0 7
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 209
32.2%
1 174
26.8%
3 131
20.2%
4 128
19.7%
0 7
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 209
32.2%
1 174
26.8%
3 131
20.2%
4 128
19.7%
0 7
 
1.1%

Mjob
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
other
258 
services
136 
at_home
135 
teacher
72 
health
48 

Length

Max length8
Median length7
Mean length6.3405239
Min length5

Characters and Unicode

Total characters4115
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowat_home
2nd rowat_home
3rd rowat_home
4th rowhealth
5th rowother

Common Values

ValueCountFrequency (%)
other 258
39.8%
services 136
21.0%
at_home 135
20.8%
teacher 72
 
11.1%
health 48
 
7.4%

Length

2025-03-19T15:21:18.726199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.774359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
other 258
39.8%
services 136
21.0%
at_home 135
20.8%
teacher 72
 
11.1%
health 48
 
7.4%

Most occurring characters

ValueCountFrequency (%)
e 857
20.8%
h 561
13.6%
t 513
12.5%
r 466
11.3%
o 393
9.6%
s 272
 
6.6%
a 255
 
6.2%
c 208
 
5.1%
v 136
 
3.3%
i 136
 
3.3%
Other values (3) 318
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3980
96.7%
Connector Punctuation 135
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 857
21.5%
h 561
14.1%
t 513
12.9%
r 466
11.7%
o 393
9.9%
s 272
 
6.8%
a 255
 
6.4%
c 208
 
5.2%
v 136
 
3.4%
i 136
 
3.4%
Other values (2) 183
 
4.6%
Connector Punctuation
ValueCountFrequency (%)
_ 135
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3980
96.7%
Common 135
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 857
21.5%
h 561
14.1%
t 513
12.9%
r 466
11.7%
o 393
9.9%
s 272
 
6.8%
a 255
 
6.4%
c 208
 
5.2%
v 136
 
3.4%
i 136
 
3.4%
Other values (2) 183
 
4.6%
Common
ValueCountFrequency (%)
_ 135
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 857
20.8%
h 561
13.6%
t 513
12.5%
r 466
11.3%
o 393
9.6%
s 272
 
6.6%
a 255
 
6.2%
c 208
 
5.1%
v 136
 
3.3%
i 136
 
3.3%
Other values (3) 318
 
7.7%

Fjob
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size40.1 KiB
other
367 
services
181 
at_home
42 
teacher
 
36
health
 
23

Length

Max length8
Median length5
Mean length6.1124807
Min length5

Characters and Unicode

Total characters3967
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowteacher
2nd rowother
3rd rowother
4th rowservices
5th rowother

Common Values

ValueCountFrequency (%)
other 367
56.5%
services 181
27.9%
at_home 42
 
6.5%
teacher 36
 
5.5%
health 23
 
3.5%

Length

2025-03-19T15:21:18.829781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.876162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
other 367
56.5%
services 181
27.9%
at_home 42
 
6.5%
teacher 36
 
5.5%
health 23
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 866
21.8%
r 584
14.7%
h 491
12.4%
t 468
11.8%
o 409
10.3%
s 362
9.1%
c 217
 
5.5%
v 181
 
4.6%
i 181
 
4.6%
a 101
 
2.5%
Other values (3) 107
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3925
98.9%
Connector Punctuation 42
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 866
22.1%
r 584
14.9%
h 491
12.5%
t 468
11.9%
o 409
10.4%
s 362
9.2%
c 217
 
5.5%
v 181
 
4.6%
i 181
 
4.6%
a 101
 
2.6%
Other values (2) 65
 
1.7%
Connector Punctuation
ValueCountFrequency (%)
_ 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3925
98.9%
Common 42
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 866
22.1%
r 584
14.9%
h 491
12.5%
t 468
11.9%
o 409
10.4%
s 362
9.2%
c 217
 
5.5%
v 181
 
4.6%
i 181
 
4.6%
a 101
 
2.6%
Other values (2) 65
 
1.7%
Common
ValueCountFrequency (%)
_ 42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 866
21.8%
r 584
14.7%
h 491
12.4%
t 468
11.8%
o 409
10.3%
s 362
9.1%
c 217
 
5.5%
v 181
 
4.6%
i 181
 
4.6%
a 101
 
2.5%
Other values (3) 107
 
2.7%

reason
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
course
285 
home
149 
reputation
143 
other
72 

Length

Max length10
Median length6
Mean length6.3112481
Min length4

Characters and Unicode

Total characters4096
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcourse
2nd rowcourse
3rd rowother
4th rowhome
5th rowhome

Common Values

ValueCountFrequency (%)
course 285
43.9%
home 149
23.0%
reputation 143
22.0%
other 72
 
11.1%

Length

2025-03-19T15:21:18.927292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:18.971305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
course 285
43.9%
home 149
23.0%
reputation 143
22.0%
other 72
 
11.1%

Most occurring characters

ValueCountFrequency (%)
o 649
15.8%
e 649
15.8%
r 500
12.2%
u 428
10.4%
t 358
8.7%
c 285
7.0%
s 285
7.0%
h 221
 
5.4%
m 149
 
3.6%
p 143
 
3.5%
Other values (3) 429
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4096
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 649
15.8%
e 649
15.8%
r 500
12.2%
u 428
10.4%
t 358
8.7%
c 285
7.0%
s 285
7.0%
h 221
 
5.4%
m 149
 
3.6%
p 143
 
3.5%
Other values (3) 429
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4096
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 649
15.8%
e 649
15.8%
r 500
12.2%
u 428
10.4%
t 358
8.7%
c 285
7.0%
s 285
7.0%
h 221
 
5.4%
m 149
 
3.6%
p 143
 
3.5%
Other values (3) 429
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4096
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 649
15.8%
e 649
15.8%
r 500
12.2%
u 428
10.4%
t 358
8.7%
c 285
7.0%
s 285
7.0%
h 221
 
5.4%
m 149
 
3.6%
p 143
 
3.5%
Other values (3) 429
10.5%

guardian
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size40.0 KiB
mother
455 
father
153 
other
 
41

Length

Max length6
Median length6
Mean length5.9368259
Min length5

Characters and Unicode

Total characters3853
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmother
2nd rowfather
3rd rowmother
4th rowmother
5th rowfather

Common Values

ValueCountFrequency (%)
mother 455
70.1%
father 153
 
23.6%
other 41
 
6.3%

Length

2025-03-19T15:21:19.021680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:19.063262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mother 455
70.1%
father 153
 
23.6%
other 41
 
6.3%

Most occurring characters

ValueCountFrequency (%)
t 649
16.8%
h 649
16.8%
e 649
16.8%
r 649
16.8%
o 496
12.9%
m 455
11.8%
f 153
 
4.0%
a 153
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3853
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 649
16.8%
h 649
16.8%
e 649
16.8%
r 649
16.8%
o 496
12.9%
m 455
11.8%
f 153
 
4.0%
a 153
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3853
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 649
16.8%
h 649
16.8%
e 649
16.8%
r 649
16.8%
o 496
12.9%
m 455
11.8%
f 153
 
4.0%
a 153
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 649
16.8%
h 649
16.8%
e 649
16.8%
r 649
16.8%
o 496
12.9%
m 455
11.8%
f 153
 
4.0%
a 153
 
4.0%

traveltime
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
1
366 
2
213 
3
54 
4
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 366
56.4%
2 213
32.8%
3 54
 
8.3%
4 16
 
2.5%

Length

2025-03-19T15:21:19.105934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:19.145589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 366
56.4%
2 213
32.8%
3 54
 
8.3%
4 16
 
2.5%

Most occurring characters

ValueCountFrequency (%)
1 366
56.4%
2 213
32.8%
3 54
 
8.3%
4 16
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 366
56.4%
2 213
32.8%
3 54
 
8.3%
4 16
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 366
56.4%
2 213
32.8%
3 54
 
8.3%
4 16
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 366
56.4%
2 213
32.8%
3 54
 
8.3%
4 16
 
2.5%

studytime
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
2
305 
1
212 
3
97 
4
35 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 305
47.0%
1 212
32.7%
3 97
 
14.9%
4 35
 
5.4%

Length

2025-03-19T15:21:19.189251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:19.227559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 305
47.0%
1 212
32.7%
3 97
 
14.9%
4 35
 
5.4%

Most occurring characters

ValueCountFrequency (%)
2 305
47.0%
1 212
32.7%
3 97
 
14.9%
4 35
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 305
47.0%
1 212
32.7%
3 97
 
14.9%
4 35
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 305
47.0%
1 212
32.7%
3 97
 
14.9%
4 35
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 305
47.0%
1 212
32.7%
3 97
 
14.9%
4 35
 
5.4%

failures
Categorical

Imbalance 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
0
549 
1
70 
2
 
16
3
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 549
84.6%
1 70
 
10.8%
2 16
 
2.5%
3 14
 
2.2%

Length

2025-03-19T15:21:19.271856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:19.310677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 549
84.6%
1 70
 
10.8%
2 16
 
2.5%
3 14
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 549
84.6%
1 70
 
10.8%
2 16
 
2.5%
3 14
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 549
84.6%
1 70
 
10.8%
2 16
 
2.5%
3 14
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 549
84.6%
1 70
 
10.8%
2 16
 
2.5%
3 14
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 549
84.6%
1 70
 
10.8%
2 16
 
2.5%
3 14
 
2.2%

schoolsup
Boolean

Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
False
581 
True
68 
ValueCountFrequency (%)
False 581
89.5%
True 68
 
10.5%
2025-03-19T15:21:19.349333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

famsup
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
True
398 
False
251 
ValueCountFrequency (%)
True 398
61.3%
False 251
38.7%
2025-03-19T15:21:19.385194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

paid
Boolean

Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
False
610 
True
 
39
ValueCountFrequency (%)
False 610
94.0%
True 39
 
6.0%
2025-03-19T15:21:19.421027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

activities
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
False
334 
True
315 
ValueCountFrequency (%)
False 334
51.5%
True 315
48.5%
2025-03-19T15:21:19.457231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

nursery
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
True
521 
False
128 
ValueCountFrequency (%)
True 521
80.3%
False 128
 
19.7%
2025-03-19T15:21:19.494980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

higher
Boolean

Imbalance 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
True
580 
False
69 
ValueCountFrequency (%)
True 580
89.4%
False 69
 
10.6%
2025-03-19T15:21:19.531362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

internet
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
True
498 
False
151 
ValueCountFrequency (%)
True 498
76.7%
False 151
 
23.3%
2025-03-19T15:21:19.571243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

romantic
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size781.0 B
False
410 
True
239 
ValueCountFrequency (%)
False 410
63.2%
True 239
36.8%
2025-03-19T15:21:19.614915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

famrel
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
4
317 
5
180 
3
101 
2
 
29
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 317
48.8%
5 180
27.7%
3 101
 
15.6%
2 29
 
4.5%
1 22
 
3.4%

Length

2025-03-19T15:21:19.657831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:19.970999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 317
48.8%
5 180
27.7%
3 101
 
15.6%
2 29
 
4.5%
1 22
 
3.4%

Most occurring characters

ValueCountFrequency (%)
4 317
48.8%
5 180
27.7%
3 101
 
15.6%
2 29
 
4.5%
1 22
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 317
48.8%
5 180
27.7%
3 101
 
15.6%
2 29
 
4.5%
1 22
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 317
48.8%
5 180
27.7%
3 101
 
15.6%
2 29
 
4.5%
1 22
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 317
48.8%
5 180
27.7%
3 101
 
15.6%
2 29
 
4.5%
1 22
 
3.4%

freetime
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
3
251 
4
178 
2
107 
5
68 
1
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 251
38.7%
4 178
27.4%
2 107
16.5%
5 68
 
10.5%
1 45
 
6.9%

Length

2025-03-19T15:21:20.015459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:20.056583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 251
38.7%
4 178
27.4%
2 107
16.5%
5 68
 
10.5%
1 45
 
6.9%

Most occurring characters

ValueCountFrequency (%)
3 251
38.7%
4 178
27.4%
2 107
16.5%
5 68
 
10.5%
1 45
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 251
38.7%
4 178
27.4%
2 107
16.5%
5 68
 
10.5%
1 45
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 251
38.7%
4 178
27.4%
2 107
16.5%
5 68
 
10.5%
1 45
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 251
38.7%
4 178
27.4%
2 107
16.5%
5 68
 
10.5%
1 45
 
6.9%

goout
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
3
205 
2
145 
4
141 
5
110 
1
48 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 205
31.6%
2 145
22.3%
4 141
21.7%
5 110
16.9%
1 48
 
7.4%

Length

2025-03-19T15:21:20.101698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:20.143165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 205
31.6%
2 145
22.3%
4 141
21.7%
5 110
16.9%
1 48
 
7.4%

Most occurring characters

ValueCountFrequency (%)
3 205
31.6%
2 145
22.3%
4 141
21.7%
5 110
16.9%
1 48
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 205
31.6%
2 145
22.3%
4 141
21.7%
5 110
16.9%
1 48
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 205
31.6%
2 145
22.3%
4 141
21.7%
5 110
16.9%
1 48
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 205
31.6%
2 145
22.3%
4 141
21.7%
5 110
16.9%
1 48
 
7.4%

Dalc
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
1
451 
2
121 
3
 
43
5
 
17
4
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 451
69.5%
2 121
 
18.6%
3 43
 
6.6%
5 17
 
2.6%
4 17
 
2.6%

Length

2025-03-19T15:21:20.188460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:20.228794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 451
69.5%
2 121
 
18.6%
3 43
 
6.6%
5 17
 
2.6%
4 17
 
2.6%

Most occurring characters

ValueCountFrequency (%)
1 451
69.5%
2 121
 
18.6%
3 43
 
6.6%
5 17
 
2.6%
4 17
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 451
69.5%
2 121
 
18.6%
3 43
 
6.6%
5 17
 
2.6%
4 17
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 451
69.5%
2 121
 
18.6%
3 43
 
6.6%
5 17
 
2.6%
4 17
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 451
69.5%
2 121
 
18.6%
3 43
 
6.6%
5 17
 
2.6%
4 17
 
2.6%

Walc
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
1
247 
2
150 
3
120 
4
87 
5
45 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 247
38.1%
2 150
23.1%
3 120
18.5%
4 87
 
13.4%
5 45
 
6.9%

Length

2025-03-19T15:21:20.272293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:20.313277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 247
38.1%
2 150
23.1%
3 120
18.5%
4 87
 
13.4%
5 45
 
6.9%

Most occurring characters

ValueCountFrequency (%)
1 247
38.1%
2 150
23.1%
3 120
18.5%
4 87
 
13.4%
5 45
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 247
38.1%
2 150
23.1%
3 120
18.5%
4 87
 
13.4%
5 45
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 247
38.1%
2 150
23.1%
3 120
18.5%
4 87
 
13.4%
5 45
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 247
38.1%
2 150
23.1%
3 120
18.5%
4 87
 
13.4%
5 45
 
6.9%

health
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
5
249 
3
124 
4
108 
1
90 
2
78 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters649
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 249
38.4%
3 124
19.1%
4 108
16.6%
1 90
 
13.9%
2 78
 
12.0%

Length

2025-03-19T15:21:20.358413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-19T15:21:20.400045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5 249
38.4%
3 124
19.1%
4 108
16.6%
1 90
 
13.9%
2 78
 
12.0%

Most occurring characters

ValueCountFrequency (%)
5 249
38.4%
3 124
19.1%
4 108
16.6%
1 90
 
13.9%
2 78
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 649
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 249
38.4%
3 124
19.1%
4 108
16.6%
1 90
 
13.9%
2 78
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common 649
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 249
38.4%
3 124
19.1%
4 108
16.6%
1 90
 
13.9%
2 78
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 249
38.4%
3 124
19.1%
4 108
16.6%
1 90
 
13.9%
2 78
 
12.0%

absences
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6594761
Minimum0
Maximum32
Zeros244
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-03-19T15:21:20.443617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile12
Maximum32
Range32
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6407588
Coefficient of variation (CV)1.2681484
Kurtosis5.7810776
Mean3.6594761
Median Absolute Deviation (MAD)2
Skewness2.0206937
Sum2375
Variance21.536642
MonotonicityNot monotonic
2025-03-19T15:21:20.489308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 244
37.6%
2 110
16.9%
4 93
 
14.3%
6 49
 
7.6%
8 42
 
6.5%
10 21
 
3.2%
1 12
 
1.8%
12 12
 
1.8%
5 12
 
1.8%
16 10
 
1.5%
Other values (14) 44
 
6.8%
ValueCountFrequency (%)
0 244
37.6%
1 12
 
1.8%
2 110
16.9%
3 7
 
1.1%
4 93
 
14.3%
5 12
 
1.8%
6 49
 
7.6%
7 3
 
0.5%
8 42
 
6.5%
9 7
 
1.1%
ValueCountFrequency (%)
32 1
 
0.2%
30 1
 
0.2%
26 1
 
0.2%
24 1
 
0.2%
22 2
 
0.3%
21 2
 
0.3%
18 3
 
0.5%
16 10
1.5%
15 2
 
0.3%
14 8
1.2%

G1
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.399076
Minimum0
Maximum19
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-03-19T15:21:20.533975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median11
Q313
95-th percentile16
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7452651
Coefficient of variation (CV)0.24083226
Kurtosis0.03663823
Mean11.399076
Median Absolute Deviation (MAD)2
Skewness-0.0027736369
Sum7398
Variance7.5364806
MonotonicityNot monotonic
2025-03-19T15:21:20.582914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 95
14.6%
11 91
14.0%
12 82
12.6%
13 72
11.1%
14 71
10.9%
9 65
10.0%
8 42
6.5%
15 35
 
5.4%
7 33
 
5.1%
16 22
 
3.4%
Other values (7) 41
6.3%
ValueCountFrequency (%)
0 1
 
0.2%
4 2
 
0.3%
5 5
 
0.8%
6 9
 
1.4%
7 33
 
5.1%
8 42
6.5%
9 65
10.0%
10 95
14.6%
11 91
14.0%
12 82
12.6%
ValueCountFrequency (%)
19 1
 
0.2%
18 7
 
1.1%
17 16
 
2.5%
16 22
 
3.4%
15 35
 
5.4%
14 71
10.9%
13 72
11.1%
12 82
12.6%
11 91
14.0%
10 95
14.6%

G2
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.570108
Minimum0
Maximum19
Zeros7
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-03-19T15:21:20.634198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4
Q110
median11
Q313
95-th percentile17
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.9136387
Coefficient of variation (CV)0.25182468
Kurtosis1.6624648
Mean11.570108
Median Absolute Deviation (MAD)2
Skewness-0.36028265
Sum7509
Variance8.4892903
MonotonicityNot monotonic
2025-03-19T15:21:20.687996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 103
15.9%
12 86
13.3%
10 83
12.8%
13 80
12.3%
9 72
11.1%
14 54
8.3%
8 40
 
6.2%
15 38
 
5.9%
16 25
 
3.9%
17 20
 
3.1%
Other values (6) 48
7.4%
ValueCountFrequency (%)
0 7
 
1.1%
5 3
 
0.5%
6 7
 
1.1%
7 16
 
2.5%
8 40
 
6.2%
9 72
11.1%
10 83
12.8%
11 103
15.9%
12 86
13.3%
13 80
12.3%
ValueCountFrequency (%)
19 1
 
0.2%
18 14
 
2.2%
17 20
 
3.1%
16 25
 
3.9%
15 38
 
5.9%
14 54
8.3%
13 80
12.3%
12 86
13.3%
11 103
15.9%
10 83
12.8%

G3
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.906009
Minimum0
Maximum19
Zeros15
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-03-19T15:21:20.739674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q110
median12
Q314
95-th percentile17
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2306562
Coefficient of variation (CV)0.27134669
Kurtosis2.7122043
Mean11.906009
Median Absolute Deviation (MAD)2
Skewness-0.91290935
Sum7727
Variance10.43714
MonotonicityNot monotonic
2025-03-19T15:21:20.789607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 104
16.0%
10 97
14.9%
13 82
12.6%
12 72
11.1%
14 63
9.7%
15 49
7.6%
16 36
 
5.5%
9 35
 
5.4%
8 35
 
5.4%
17 29
 
4.5%
Other values (7) 47
7.2%
ValueCountFrequency (%)
0 15
 
2.3%
1 1
 
0.2%
5 1
 
0.2%
6 3
 
0.5%
7 10
 
1.5%
8 35
 
5.4%
9 35
 
5.4%
10 97
14.9%
11 104
16.0%
12 72
11.1%
ValueCountFrequency (%)
19 2
 
0.3%
18 15
 
2.3%
17 29
 
4.5%
16 36
 
5.5%
15 49
7.6%
14 63
9.7%
13 82
12.6%
12 72
11.1%
11 104
16.0%
10 97
14.9%

Interactions

2025-03-19T15:21:17.496521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:13.881951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:16.882562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.085149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.289594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.537650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:15.601038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:16.923814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.125367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.331818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.577081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:16.242641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:16.961402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.166023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.371107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.618589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:16.569317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.002793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.207145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.413002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.658728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:16.839375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.043614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.248796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-19T15:21:17.454663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-19T15:21:20.845422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
DalcFeduFjobG1G2G3MeduMjobPstatusWalcabsencesactivitiesaddressagefailuresfamrelfamsizefamsupfreetimegooutguardianhealthhigherinternetnurserypaidreasonromanticschoolschoolsupsexstudytimetraveltime
Dalc1.0000.0000.0000.0960.1270.1820.0000.0000.0210.4070.1460.0150.0340.1480.0940.0730.0290.0990.0880.1220.0840.0450.1160.0000.0660.0000.0630.0930.0300.0000.2880.1140.061
Fedu0.0001.0000.2680.1090.1180.1280.3800.2040.0390.0510.0000.0840.1450.0670.0770.0000.0200.1160.0000.0000.0570.0670.1930.1960.0810.0530.0000.0970.2000.0160.0620.0310.128
Fjob0.0000.2681.0000.1300.0430.0680.1840.2140.0580.0570.0660.0000.0460.0000.0000.0710.0380.0770.0000.0000.1000.0000.0860.0800.0000.0000.0650.0000.1640.0770.0240.0450.027
G10.0960.1090.1301.0000.8930.8830.1230.1210.0770.064-0.1700.0760.133-0.1670.2760.0430.0000.0000.0720.0830.0650.0000.4090.1290.0000.0000.0950.0000.3480.1270.0920.1710.035
G20.1270.1180.0430.8931.0000.9440.1280.1190.0750.079-0.1640.0800.191-0.1060.2720.1080.0000.0000.0210.0830.0700.0640.3600.1560.1320.0000.1050.0780.3200.1180.1040.1360.100
G30.1820.1280.0680.8830.9441.0000.1400.1250.0000.107-0.1590.0960.186-0.0660.3060.0550.0000.0530.0820.1110.0250.0750.3790.1410.0860.0630.1210.0420.3320.1490.0970.1530.080
Medu0.0000.3800.1840.1230.1280.1401.0000.3750.0690.0000.0000.1220.1830.0290.1350.0000.0000.0960.0000.0000.0780.0000.2030.2630.1390.0850.0790.0490.2720.0000.0990.0000.144
Mjob0.0000.2040.2140.1210.1190.1250.3751.0000.0000.0000.0000.1050.1890.0320.0620.0000.0000.0770.0000.0350.1110.0000.1840.2930.0740.0000.0960.0380.2290.0520.1490.0000.108
Pstatus0.0210.0390.0580.0770.0750.0000.0690.0001.0000.0680.1990.0890.0810.0000.0740.0290.2310.0000.0600.0000.1620.0000.0000.0370.0000.0000.0160.0290.0000.0000.0450.0730.000
Walc0.4070.0510.0570.0640.0790.1070.0000.0000.0681.0000.0860.0680.0000.0700.0530.0390.0390.0750.1100.2170.0000.0330.0560.0250.0150.0000.0000.0000.0000.1430.3310.1450.099
absences0.1460.0000.066-0.170-0.164-0.1590.0000.0000.1990.0861.0000.0230.0650.1240.1000.1040.0000.0000.0000.0210.1360.0350.1640.0690.1280.0000.0000.0000.1510.0000.0120.0000.000
activities0.0150.0840.0000.0760.0800.0960.1220.1050.0890.0680.0231.0000.0000.0000.0780.0000.0000.0000.1590.0580.0000.0060.0070.0680.0000.0440.1550.0380.0760.0000.1150.0400.000
address0.0340.1450.0460.1330.1910.1860.1830.1890.0810.0000.0650.0001.0000.0000.0980.0000.0160.0000.0620.0000.0000.0000.0600.1670.0000.0000.1580.0000.3490.0000.0000.0830.347
age0.1480.0670.000-0.167-0.106-0.0660.0290.0320.0000.0700.1240.0000.0001.0000.3150.0000.0940.0590.0000.0610.3710.0000.2810.0000.0330.1010.0000.1580.0900.1660.0000.0000.000
failures0.0940.0770.0000.2760.2720.3060.1350.0620.0740.0530.1000.0780.0980.3151.0000.0600.0920.0000.0440.0000.1950.0000.3140.0810.0350.0430.0930.0470.1640.0000.0540.0880.076
famrel0.0730.0000.0710.0430.1080.0550.0000.0000.0290.0390.1040.0000.0000.0000.0601.0000.0000.0000.0660.0680.0350.0720.0030.1140.0320.0000.0000.0590.0870.0410.0560.0530.000
famsize0.0290.0200.0380.0000.0000.0000.0000.0000.2310.0390.0000.0000.0160.0940.0920.0001.0000.0000.0290.0000.0000.0000.0000.0000.0880.0180.0000.0000.0000.0320.0860.0000.000
famsup0.0990.1160.0770.0000.0000.0530.0960.0770.0000.0750.0000.0000.0000.0590.0000.0000.0001.0000.0570.0570.0000.0000.0700.0560.0000.0780.0730.0000.0460.0580.1200.1450.062
freetime0.0880.0000.0000.0720.0210.0820.0000.0000.0600.1100.0000.1590.0620.0000.0440.0660.0290.0571.0000.2230.0000.0510.1120.0550.0730.0240.0430.0660.1200.0510.1540.1220.000
goout0.1220.0000.0000.0830.0830.1110.0000.0350.0000.2170.0210.0580.0000.0610.0000.0680.0000.0570.2231.0000.0000.0470.1250.0600.0510.0590.0000.0000.0820.0000.0400.0980.062
guardian0.0840.0570.1000.0650.0700.0250.0780.1110.1620.0000.1360.0000.0000.3710.1950.0350.0000.0000.0000.0001.0000.0650.1900.0000.0860.0450.0000.1230.0370.0000.0000.0000.045
health0.0450.0670.0000.0000.0640.0750.0000.0000.0000.0330.0350.0060.0000.0000.0000.0720.0000.0000.0510.0470.0651.0000.0000.0000.0000.0320.0500.0000.0000.0000.1210.0830.000
higher0.1160.1930.0860.4090.3600.3790.2030.1840.0000.0560.1640.0070.0600.2810.3140.0030.0000.0700.1120.1250.1900.0001.0000.0510.0000.0000.1070.0860.1250.0670.0360.2200.084
internet0.0000.1960.0800.1290.1560.1410.2630.2930.0370.0250.0690.0680.1670.0000.0810.1140.0000.0560.0550.0600.0000.0000.0511.0000.0000.0000.1320.0000.2340.0000.0480.0000.183
nursery0.0660.0810.0000.0000.1320.0860.1390.0740.0000.0150.1280.0000.0000.0330.0350.0320.0880.0000.0730.0510.0860.0000.0000.0001.0000.0000.0000.0000.0000.0000.0050.0000.000
paid0.0000.0530.0000.0000.0000.0630.0850.0000.0000.0000.0000.0440.0000.1010.0430.0000.0180.0780.0240.0590.0450.0320.0000.0000.0001.0000.0740.0000.0000.0000.0610.0000.014
reason0.0630.0000.0650.0950.1050.1210.0790.0960.0160.0000.0000.1550.1580.0000.0930.0000.0000.0730.0430.0000.0000.0500.1070.1320.0000.0741.0000.0240.2760.0270.0330.1110.089
romantic0.0930.0970.0000.0000.0780.0420.0490.0380.0290.0000.0000.0380.0000.1580.0470.0590.0000.0000.0660.0000.1230.0000.0860.0000.0000.0000.0241.0000.0570.0800.0990.0940.000
school0.0300.2000.1640.3480.3200.3320.2720.2290.0000.0000.1510.0760.3490.0900.1640.0870.0000.0460.1200.0820.0370.0000.1250.2340.0000.0000.2760.0571.0000.1110.0690.1260.299
schoolsup0.0000.0160.0770.1270.1180.1490.0000.0520.0000.1430.0000.0000.0000.1660.0000.0410.0320.0580.0510.0000.0000.0000.0670.0000.0000.0000.0270.0800.1111.0000.0990.0980.059
sex0.2880.0620.0240.0920.1040.0970.0990.1490.0450.3310.0120.1150.0000.0000.0540.0560.0860.1200.1540.0400.0000.1210.0360.0480.0050.0610.0330.0990.0690.0991.0000.2490.000
studytime0.1140.0310.0450.1710.1360.1530.0000.0000.0730.1450.0000.0400.0830.0000.0880.0530.0000.1450.1220.0980.0000.0830.2200.0000.0000.0000.1110.0940.1260.0980.2491.0000.040
traveltime0.0610.1280.0270.0350.1000.0800.1440.1080.0000.0990.0000.0000.3470.0000.0760.0000.0000.0620.0000.0620.0450.0000.0840.1830.0000.0140.0890.0000.2990.0590.0000.0401.000

Missing values

2025-03-19T15:21:17.735863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-19T15:21:17.897462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardiantraveltimestudytimefailuresschoolsupfamsuppaidactivitiesnurseryhigherinternetromanticfamrelfreetimegooutDalcWalchealthabsencesG1G2G3
0GPF18UGT3A44at_hometeachercoursemother220yesnononoyesyesnono434113401111
1GPF17UGT3T11at_homeothercoursefather120noyesnononoyesyesno533113291111
2GPF15ULE3T11at_homeotherothermother120yesnononoyesyesyesno4322336121312
3GPF15UGT3T42healthserviceshomemother130noyesnoyesyesyesyesyes3221150141414
4GPF16UGT3T33otherotherhomefather120noyesnonoyesyesnono4321250111313
5GPM16ULE3T43servicesotherreputationmother120noyesnoyesyesyesyesno5421256121213
6GPM16ULE3T22otherotherhomemother120nonononoyesyesyesno4441130131213
7GPF17UGT3A44otherteacherhomemother220yesyesnonoyesyesnono4141112101313
8GPM15ULE3A32servicesotherhomemother120noyesnonoyesyesyesno4221110151617
9GPM15UGT3T34otherotherhomemother120noyesnoyesyesyesyesno5511150121213
schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardiantraveltimestudytimefailuresschoolsupfamsuppaidactivitiesnurseryhigherinternetromanticfamrelfreetimegooutDalcWalchealthabsencesG1G2G3
639MSM19RGT3T11otherservicesothermother211nonononoyesyesnono4321350580
640MSM18RGT3T42otherotherhomefather211nonoyesnoyesyesnono5434330770
641MSF18RGT3T22at_homeotherothermother230nonononoyesyesnono5331340141715
642MSF17UGT3T43teacherotherothermother220nonononoyesyesyesno55411106911
643MSF18RGT3T44teacherat_homereputationmother310noyesnoyesyesyesyesyes44322547910
644MSF19RGT3T23servicesothercoursemother131nononoyesnoyesyesno5421254101110
645MSF18ULE3T31teacherservicescoursemother120noyesnonoyesyesyesno4341114151516
646MSF18UGT3T11otherothercoursemother220nononoyesyesyesnono111115611129
647MSM17ULE3T31servicesservicescoursemother210nononononoyesyesno2453426101010
648MSM18RLE3T32servicesothercoursemother310nononononoyesyesno4413454101111